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BackgroundIn the United States, black men who have sex with men (BMSM) are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) can reduce HIV incidence. However, real-world implementation of PrEP outside of clinical trials has identified racial disparities in PrEP awareness, uptake, and adherence. In the context of a long history of medical mistrust and power imbalances between scientists and community members, strategies to increase uptake of PrEP among BMSM should consider ways to ensure messages address the needs and priorities of the community. Crowdsourcing contests shift traditional individual tasks to a large group and may enhance community engagement. ObjectiveThis paper describes the research protocol of a contest approach to soliciting PrEP promotion messages among BMSM in Baltimore. MethodsOpen-contest implementation and evaluation will proceed as follows: (1) organize a community steering group; (2) develop platforms to solicit crowd input; (3) engage the community to contribute ideas through a Web-based forum and in-person events; (4) evaluate contest entries using both community panel judge assessment and crowd voting; (5) utilize mixed methods to evaluate feasibility, acceptability, and community engagement; and (6) disseminate contest results. ResultsThis study was funded by the National Institutes of Health (National Institute of Mental Health: R34MH116725) in May 2018 and was approved by the institutional review board in April 2018. The open contest started in February 2019, and data analyses for the mixed method evaluation are expected to complete in December 2019. ConclusionsThe contest will potentially bring new ideas in developing more impactful and locally defined PrEP promotion campaigns. We will determine whether an open-contest approach is acceptable among BMSM in Baltimore. If successful, this study can inform future projects using a similar approach on how to identify and implement programs and policies that are more responsive to community needs and that build up community assets. International Registered Report Identifier (IRRID)DERR1-10.2196/15590

Abstract Background Evolutionary radiation is widely recognized as a mode of species diversification, but the drivers of the rapid diversification of fungi remain largely unknown. Here, we used Amanitaceae, one of the most diverse families of macro-fungi, to investigate the mechanism underlying its diversification. Results The ancestral state of the nutritional modes was assessed based on phylogenies obtained from fragments of 36 single-copy genes and stable isotope analyses of carbon and nitrogen. Moreover, a number of time-, trait-, and paleotemperature-dependent models were employed to investigate if the acquisition of ectomycorrhizal (ECM) symbiosis and climate changes promoted the diversification of Amanitaceae. The results indicate that the evolution of ECM symbiosis has a single evolutionary origin in Amanitaceae. The earliest increase in diversification coincided with the acquisition of the ECM symbiosis with angiosperms in the middle Cretaceous. The recent explosive diversification was primarily triggered by the host-plant switches from angiosperms to the mixed forests dominated by Fagaceae, Salicaceae, and Pinaceae or to Pinaceae. Conclusions Our study provides a good example of integrating phylogeny, nutritional mode evolution, and ecological analyses for deciphering the mechanisms underlying fungal evolutionary diversification. This study also provides new insights into how the transition to ECM symbiosis has driven the diversification of fungi.

Abstract The demand for high-capacity batteries with long cycle life and safety has been increasing owing to the expanding mid-to-large battery market. Li–S batteries are suitable energy-storage devices because of their reversibility, high theoretical capacity, and inexpensive construction materials. However, their performance is limited by various factors, including the shuttle effect and dendrite growth at the anode. Here, an integrated electrode for use in all-solid-state (ASS) Li–S batteries was formed via hot pressing. In detail, S particles dispersed in a functionalized reduced graphite oxide (rGO) cathode with a binder-less polymer electrolyte (PE) and a dual-anion ionic liquid-containing cross-linked poly(ethylene oxide)–Li bis(fluoromethanesulfonyl)imide–N-butyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl) imide-based solid polymer electrolyte (SPE, PEO–LiFSI0.1(Pyr14TFSI)0.4) were hot-pressed into an integrated electrode, which serves as both the cathode and electrolyte. The resulting S/rGO-based solid-state Li–S batteries exhibited more stable performance than Li–S batteries using liquid electrolytes did, indicating that the dual-anion SPE layer effectively suppressed dendritic Li formation and the shuttle effect with high ionic conductivity. At 0.1 C, the battery discharge capacities were 957 and 576 mAh g−1 in the first cycle and after 100 cycles, respectively. At 1 C, the reversible capacity was 590 and 417 mAh g−1 in the first cycle and after 100 cycles, respectively (capacity retention = 71%). Therefore, the proposed S/rGO/PE//LiFSI0.1(Pyr14TFSI)0.4-integrated electrodes are beneficial for ASS Li–S batteries.

Abstract To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

IntroductionThe genus Laccaria has been reported from temperate and tropical areas and is an important constituent in forest ecosystems. However, the species diversity of Laccaria in Southwest China (Yunnan) has been underestimated.MethodsIn this paper, descriptions based on morphological and multi-gene sequence data from internal transcribed spacer (ITS) region, large subunit ribosomal RNA gene (nrLSU), translation elongation factor 1-α (TEF1α) and the polymerase II second largest subunit (RPB2) of three new Laccaria species from Southwest China (Yunnan) are reported.ResultsTwo of these were characterized by orange pileus and globose to subglobose basidiospores: L. cinnabarina and L. spinulosa. While L. cinnabarina has orange red colored basidiocarps with conspicuously pellucid-striate pattern, and a fibrillose stipe with longitudinally striations, L. spinulosa has a brownish orange to brown fruiting body with light white pruinae and 2-spored basidia. Laccaria longistriata is characterized by brown to flesh-colored basidioma, prominently striate to sulcate pileus and globose to subglobose basidiospores.DiscussionThe three new species were described, illustrated and compared with closely related species in morphology and phylogeny.

Despite recent emphasis on couples-based HIV prevention interventions, efficacious interventions for Latino male couples have yet to be tested. The study examined the feasibility and acceptability of the Connecting Latinos en Pareja (CLP) intervention, a couples-based HIV prevention intervention for Latino male couples. This pilot demonstrated high feasibility, meeting targets for recruitment, retention, and intervention completion. We recruited a diverse cohort of 46 individuals/23 couples with a retention rate of 80% over 6 months and a 100% intervention completion rate in both conditions (four structured couple sessions in each condition). This pilot RCT was not powered to detect significant intervention impact on the primary outcome; however, there was a significant increase in relationship functioning among couples in the intervention group relative to controls and promising trends in changes in several key outcome and mediating variables. Secondary analysis documented trends in the expected directions for several other key hypothesized mechanisms of action (stimulant use, psychological symptoms, quality of life) as well as the primary outcome of proportion of protected sex acts (overall and broken out by main vs. outside partners). Qualitative exit interviews revealed high levels of acceptability of the CLP intervention. Participants highlighted affective component of the intervention and perceived effectiveness in improving dyadic communication skills and safer sex practices. Our findings show that a pilot trial of CLP is highly feasible and acceptable, with CLP demonstrating promising evidence of changes in key intervention mechanisms.

BackgroundAlcohol use is a risk factor for the acquisition of human immunodeficiency virus (HIV) among African American men who have sex with men (MSM). Mobile phone-based ecological momentary assessments (EMA) could minimize bias due to retrospective recall and thus provide a better understanding of the social and structural context of alcohol use and its relationship with HIV-related risk behaviors in this population as well as other highly stigmatized populations. ObjectiveWe describe the study design and the implementation, feasibility, reactivity, and acceptability of an EMA study of alcohol use and HIV-related behaviors among African American MSM in Baltimore. MethodsParticipants were recruited through flyers and word-of-mouth in Baltimore from September 2013 to November 2014. Each participant was loaned an Android smartphone and instructed to respond to multiple prompts from the mobile app for 4 weeks. Data were collected through (1) random prompts delivered three times daily assessing participants’ location, activity, mood, and social context, (2) daily prompts capturing drinking and sex events occurring in the past 24 hours, and (3) event-contingent responses collecting participants’ self-reported episodes of drinking. ResultsA total of 16 participants enrolled in the study. The current analyses focused on 15 participants who completed at least 24 days of follow-up (mean follow-up time 29 days; range 24-35 days). Study participants (N=15) were a median 38 years of age (range 27-62 years) with low levels of income and educational attainment. Ten individuals self-reported living with HIV/AIDS, over half reported drinking alcohol at least 2-3 times a week, and a third reported binge drinking (ie, 6 or more drinks on one occasion) on a weekly basis. Based on the Alcohol Use Disorders Identification Test (AUDIT) score, nearly half were classified as hazardous drinkers (score 8-15) and a fifth were likely dependent (score ≥16). A total of 140 participant-initiated events were reported, and 75% of 1308 random prompts and 81% of 436 daily prompts delivered were answered. Of seven devices used during the study, five were reported lost by participants. We did not observe strong reactivity effects, and self-reported acceptability to study procedures was uniformly favorable. ConclusionsThis study provides evidence to support the feasibility and acceptability of using EMA methods for collecting data on alcohol use among African American men who have sex with men living in urban settings. These data provide the basis for future studies of EMA-informed mHealth interventions to promote the reduction of substance use and HIV risk-taking behaviors among African American MSM living in urban settings.

Based on the unique properties of optical Fano resonance and plasmonic-waveguide coupling systems, this paper explores a novel refractive index concentration sensor structure. The sensor structure is composed of a metal–insulator–metal (MIM) waveguide and two identically shaped and sized double-tooth ring couplers (DTR). The performance structure of the nanoscale refractive index sensor with DTR cavity was comprehensively assessed using the finite element method (FEM). Due to the impact of various geometric parameters on the sensing characteristics, including the rotation angles, the widths between the double-tooth rings, and the gaps between the cavity and the waveguide, we identified an optimal novel refractive index sensor structure that boasts the best performance indices. Finally, the DTR cavity sensor achieved a sensitivity of 4137 nm/RIU and Figure of merit (FOM) of 59.1. Given the high complexity and sensitivity of the overall structure, this nanoscale refractive index sensor can be applied to the detection of oil concentration in industrial oil–water mixtures, yielding highly precise results.

IntroductionTo assess the performance of the European Thyroid Association Thyroid Imaging and Reporting Data System (EU-TIRADS) and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS), which combine risk stratification systems for thyroid nodules (TN-RSS) and cervical lymph nodes (LN-RSS) in diagnosing malignant and metastatic thyroid cancer in a single referral center.MethodsWe retrospectively analyzed 2,055 consecutive patients who underwent thyroidectomy or fine-needle aspiration (FNA) from January 2021 to December 2022. TNs and LNs were categorized according to the ultrasonography (US) features of EU-TIRADS and K-TIRADS, respectively. The diagnostic performance and postponed malignancy rate (PMR) were compared with those of EU-TIRADS and K-TIRADS. PMR was defined as the number of patients with malignant nodules not recommended for biopsy among patients with cervical LN metastasis.ResultsAccording to the EU-TIRADS and K-TIRADS, for TN-RSS alone, there were no significant differences in sensitivity, specificity, accuracy, unnecessary FNA rate (UFR), missed malignancy rate (MMR), and PMR between the two TIRADSs (29.0% vs. 28.8%, 50.5% vs. 51.1%, 32.3% vs. 32.2%, 23.6% vs. 23.5%, 88.6% vs. 88.5%, and 54.2% vs. 54.5%, P > 0.05 for all). Combining the LN-RSS increased the diagnostic accuracy (42.7% vs. 32.3% in EU-TIRADS; 38.8% vs. 32.2% in K-TIRADS) and decreased the PMR (54.2% vs. 33.9% in EU-TIRADS; 54.5% vs. 39.3% in K-TIRADS). EU-TIRADS had higher sensitivity and accuracy and lower PMR than K-TIRADS (41.3% vs. 36.7%, 42.7% vs. 38.8%,33.9% vs. 39.3%, P < 0.05 for all).ConclusionsA combination of TN-RSS and LN-RSS for the management of thyroid nodules may be associated with a reduction in PMR, with enhanced sensitivity and accuracy for thyroid cancers in EU-TIRADS and K-TIRADS. These results may offer a new direction for the detection of aggressive thyroid cancers.

This study examines the atmospheric water cycle dynamics in the China-Mongolia Arid Region (CMAR), a region significantly affected by aridity. By employing a combination of Empirical Orthogonal Function (EOF) analysis, ERA5 reanalysis data, and the Dynamic Recycling Model (DRM), we investigate the spatial and temporal variations in the Precipitation Recycling Ratio (PRR) and Precipitable Water Conversion Rate (PWCR) over a forty-year period (1979–2021). Our findings reveal that both PRR and PWCR are generally higher but decreasing in most subregions of CMAR, suggesting a notable contribution of local moisture to precipitation. We also identify an increasing trend in PRR across the northwestern subregions and a decreasing trend in other areas. Similarly, PWCR exhibits an increasing trend in the northwestern and southern subregions, while decreasing elsewhere, implying a decline in water vapor conversion and recycling efficiency. Furthermore, our EOF analysis uncovers distinct spatial patterns, with dominant modes accounting for significant variances in PRR and PWCR, correlating with local variations in atmospheric moisture and advective changes. These results underscore the complex interplay between regional topography, atmospheric dynamics, and the hydrological cycle in CMAR. The insights from this study are vital for formulating effective water management strategies and adapting to climate change impacts in arid regions, holding broad implications for environmental science, climate studies, and sustainable resource management. Our findings reveal distinct spatial patterns and contrasting trends in precipitation recycling and water vapor conversion across the subregions of CMAR. This heterogeneity underscores the importance of conducting analyses at finer spatial scales to avoid contradictory conclusions that can arise from topographic influences when treating CMAR as a single unit. Future studies should focus on smaller subregions to accurately capture the intricacies of the water cycle in this topographically complex arid region.

ObjectiveWe aimed to explore the relationship between remote resistance exercise programs delivered via a smartphone application and skeletal muscle mass among elderly patients with type 2 diabetes, utilizing real-world data.MethodsThe resistance exercises were provided through Joymotion®, a web-based telerehabilitation smartphone application (Shanghai Medmotion Medical Management Co., Ltd). The primary outcome was the changes in skeletal muscle index (SMI) before and after the remote resistance exercises programs. The secondary outcomes were changes in skeletal muscle cross-sectional area (SMA), skeletal muscle radiodensity (SMD) and intermuscular adipose tissue (IMAT).ResultsA total of 101 elderly patients with type 2 diabetes were analyzed. The participants had an average age of 72.9 ± 6.11 years for males and 74.4 ± 4.39 years for females. The pre- and post-intervention SMI mean (± SE) was 31.64 ± 4.14 vs. 33.25 ± 4.22 cm2/m2 in male, and 22.72 ± 3.24 vs. 24.28 ± 3.60 cm2/m2 in female respectively (all P < 0.001). Similarly, a statistically significant improvement in SMA, IMAT, and SMD for both male and female groups were also observed respectively (P < 0.001). Multiple linear regression models showed potential confounding factors of baseline hemoglobin A1c and duration of diabetes with changes in SMI in male, while hemoglobin A1c and high density lipoprotein cholesterol with changes in SMI in female.ConclusionRemote resistance exercises programs delivered by a smartphone application were feasible and effective in helping elderly patients with type 2 diabetes to improve their skeletal muscle mass.

With the development of science and technology, the phenomenon of smartphone addiction has become very common. However, smartphone addiction has adverse consequences. To date, few studies have examined psychological crises and smartphone use motives during the coronavirus disease 2019 (COVID-19) pandemic according to age. Therefore, this study aimed to examine the influences of different types of smartphone use motives on smartphone addiction and explore the moderating effect of age on adolescents and adults. A total of 1346 participants (600 adults and 746 adolescents) completed questionnaires on their motives for smartphone use and smartphone addiction. Results indicated significant positive correlations between smartphone use motives and smartphone addiction. In the moderation model, mood regulation, social relations, pastime, and conformity significantly and directly predicted smartphone addiction; however, perceived enjoyment did not. Age played a moderating role in the prediction of smartphone addiction. Teenagers and adults have different motives for smartphone use, and different motives have different effects on adolescents and adults. Adolescents have higher coping motivation and conformity motivation than adults, and for adolescents, perceived pleasure motivation has a significant impact on smartphone addiction. For adults, perceived pleasure and social relationship motivation have a significant impact on smartphone addiction. Therefore, interventions for smartphone addiction can be developed by investigating the motives of use among different people, and age should be considered when developing interventions for smartphone addiction., Author(s): Fangfang Wen [sup.1] , Yu Ding [sup.2] , Cui Yang [sup.2] , Shuhan Ma [sup.2] , Jianli Zhu [sup.2] , Huanrui Xiao [sup.2] , Bin Zuo [sup.1] Author Affiliations: [...]

The utilization of renewable energy represents an effective way to address current issues associated with fossil fuels. Biomass is considered one type of renewable energy resources with abundant reserves on earth. However, the high oxygen contents and high degree of functionalization of biomass have hindered the direct exploitation of biomass for the production of fuels and chemicals. Considerable efforts have been devoted to developing effective deoxygenation methods capable of reducing the oxygen contents of biomass and its derivatives. The deoxydehydration (DODH) of biomass derivatives to generate olefins over oxophilic metal catalysts is considered a very useful approach in eliminating vicinal OH groups. In recent years, catalysts based on non-noble metals such as Mo, W, and V featuring good catalytic performance have emerged as promising alternatives to classical noble Re-based catalysts for DODH. This review aims to summarize the progress on the DODH of biomass-derived vicinal diols catalyzed by non-noble metals such as Mo, W, and V, with an emphasis on the preparation of catalysts, optimization of experimental conditions, and mechanistic studies. By surveying the performance of non-noble metal catalysts, key factors that determine the DODH activity were proposed, including the choice of reductant, the electronic and steric effects of ligand, and the interaction between solid support and metal center. The latter two could adjust the redox properties of metal centers by directly bonding with ligand or solid support.

Abstract Accurate water pollution prediction is an important basis for water environment prevention and control. The uncertainty of input variables and the nonstationary and nonlinear characteristics of water pollution series hinder the accuracy and reliability of water pollution prediction. This study proposed a novel water pollution prediction model (RF-CEEMD-LSTM) to improve the performance of water pollution prediction by combining advantages of the random forest (RF) and Long short-term memory (LSTM) models and Complementary ensemble empirical mode decomposition (CEEMD). The experimental results based on measured data show that the proposed RF-CEEMD-LSTM model can accurately predict water pollution trends, with a mean ab-solute percentage error (MAPE) of less than 8%. The RMSE of the RF-CEEMD-LSTM model is reduced by 62.6%, 39.9%, and 15.5% compared to those of the LSTM, RF-LSTM, and CEEMD-LSTM models, respectively, proving that the proposed method has good advantages in predicting non-linear and nonstationary water pollution sequences. The driving force analysis results showed that TN has the most significant impact on water pollution prediction. The research results could provide references for identifying and explaining water pollution variables and improving water pollution prediction method.

Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC.

As a critical component of electrostatic capacitors, the polymer dielectric directly affects the performance of the capacitor. In this work, Polycarbonate (PC)/Polyvinylidene fluoride (PVDF) asymmetric bilayer polymer dielectrics were prepared, and the influence of different polymer materials’ barrier characteristics on various electrical properties of composite dielectrics was studied by changing the direction of applied electric fields. Research has found that the dielectric constant of a composite dielectric is between PVDF and PC (approximately 4.8 at 10 Hz) and is independent of the relative position of PVDF and PC in the dielectric. However, the relative position of PC and PVDF has a significant impact on the energy-storage characteristics of composite dielectrics. When PVDF comes into contact with the negative electrode, even though PC has a higher hole barrier, the composite dielectric can only withstand a maximum electric-field strength of 400 MV/m, which is much lower than the maximum electric-field strength that pure PC can withstand (520 MV/m), and it only achieves an energy-storage density of 3.7 J/cm3. When the PC comes into contact with the negative electrode, the high electron barrier of the PC effectively suppresses the injection of electrons at the electrode. It can withstand the same electric-field strength as PC (520 MV/m), achieving an energy-storage density of 5.48 J/cm3, which is 1.46 times that of pure PC and 1.64 times that of PVDF. This experiment effectively combined the advantages of PC and PVDF by utilizing the electron/hole barrier of polymer materials to obtain a fully organic dielectric with excellent energy-storage performance.

In this work, a MoS2/C heterostructure was designed and prepared through an in situ composite method. The introduction of carbon during the synthesis process altered the morphology and size of MoS2, resulting in a reduction in the size of the flower-like structures. Further, by varying the carbon content, a series of characterization methods were employed to study the structure and electrochemical lithium storage performance of the composites, revealing the effect of carbon content on the morphology, structure characteristics, and electrochemical performance of MoS2/C composites. The experimental setup included three sample groups: MCS, MCM, and MCL, with glucose additions of 0.24 g, 0.48 g, and 0.96 g, respectively. With increasing carbon content, the size of MoS2 initially decreases, then increases. Among these, the MCM sample exhibits the optimal structure, characterized by smaller MoS2 dimensions with less variation. The electrochemical results showed that MCM exhibited excellent electrochemical lithium storage performance, with reversible specific capacities of 956.8, 767.4, 646.1, and 561.4 mAh/g after 10 cycles at 100, 200, 500, and 1000 mA/g, respectively.

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data.

Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar target image samples, a novel augmentation method employing self-training with a Disrupted Student model is designed (DS-SIAUG). The process begins by inputting a dataset of side-scan sonar target images, followed by augmenting the samples through an adversarial network consisting of the DDPM (Denoising Diffusion Probabilistic Model) and the YOLO (You Only Look Once) detection model. Subsequently, the Disrupted Student model is used to filter out representative target images. These selected images are then reused as a new dataset to repeat the adversarial filtering process. Experimental results indicate that using the Disrupted Student model for selection achieves a target recognition accuracy comparable to manual selection, improving the accuracy of intelligent target recognition by approximately 5% over direct adversarial network augmentation.

Bereavement is a commonly experienced grief event; however, bereavement can also trigger a number of psychological consequences, such as prolonged grief disorder (PGD). At present, the differences in prolonged grief disorder symptoms (PGD symptoms) among various individuals and how those symptoms relate to cognitive variables are unclear. In the present study, 817 Chinese college students with bereavement experience were selected as participants. Based on the evaluation results of their irrational beliefs, bereavement-related irrational beliefs, basic world assumptions, and PGD symptoms, an individual-centered latent profile analysis was used to divide a group with PGD symptoms into several subgroups and comprehensively examine the relationships between these subgroups and cognitive variables. (1) The severity of PGD symptoms among Chinese college students can be categorized into three subgroups: mild, moderate, and severe. (2) Cognitive variables such as irrational beliefs and basic world assumptions were all found to correlate with the severity of PGD symptoms; bereavement-related irrational beliefs was the variable with the largest correlation. However, for the first time, this study found that different dimensions of basic world assumptions had different directions of correlation, based on the severity of the PGD symptoms. Justice, control, randomness, and self-control had significantly positive correlations. Conversely, benevolence of the world, benevolence of people, and worthiness of the self had significantly negative correlations. These results have important reference value for cognitive behavioral therapy (CBT) treatment and interventions for PGD issues in Chinese college students.

In this paper, a novel nano sensor structure is proposed, which consists of a metal-insulator-metal waveguide (MIM) with rectangular baffles and a water droplet cavity with rectangular stubs (WDCRS). The WDCRS structure optimizes the sensitivity of a single water droplet cavity and makes the transmission curve clearer and smoother. The transmission characteristics of WDCRS structure were simulated using finite element method (FEM). The transmission characteristics of the exported structure were analyzed in detail. In addition, the influence of structural geometric parameters on sensing performance was also studied, and it was found that the size of the water droplet cavity is a key factor in improving sensitivity. When applied to a refractive index sensor, the structure achieves a sensitivity of up to 2,300 nm/RIU with a corresponding figure of merit (FOM) of 60.5. These works provide some ideas for the design of high-performance nanostructures and multiple Fano resonance excitation structures.